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Python for Data Science Tutorial | Quickstart : A Complete Guide

Last updated on 11th Aug 2022, Blog, Tutorials

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Vimala Raman (Python / Data Science Engineer )

Vimala Raman has over 5+ years of experience as a Python / Data Science Engineer. She provides extensive expertise in Python, R, Weka, and Matlab and a deep understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests

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Python in Data Science:

Data science programming requires a very versatile yet flexible language that is simple to write code in but can handle highly complex mathematical processing. Python is best suited for such needs because it has already established itself as a language for both general and scientific computing. Furthermore, it is constantly being upgraded in the form of new additions to its plethora of libraries aimed at various programming requirements. The following sections will go over the features of Python that make it the preferred language for data science.

  • A simple and easy-to-learn language with fewer lines of code than other similar languages such as R. Its simplicity also makes it robust enough to handle complex scenarios with minimal code and much less confusion about the program’s overall flow.
  • Because it is cross-platform, the same code can be used in multiple environments without modification. As a result, it is ideal for use in a multi-environment setup.
  • It runs faster than other data analysis languages, such as R and MATLAB.
  • Its excellent memory management capability, particularly garbage collection, allows it to manage very large volumes of data transformation, slicing, dicing, and visualization gracefully.
  • Most importantly, Python has a vast library of libraries that serve as specialized analysis tools. The NumPy package, for example, deals with scientific computing, and its array requires far less memory than the standard Python list for managing numeric data. And the number of such packages is constantly increasing.
  • Python has packages that can directly use code written in other languages such as Java or C. This aids in optimizing code performance by reusing existing code from other languages whenever a better result is obtained.

Why should you learn Python for data science?

Python

Python is the preferred programming language for data scientists. Despite the fact that it was not the first primary programming language, its popularity has grown over time.

  • It surpassed R on Kaggle, the premier platform for data science competitions, in 2016.
  • It surpassed R in the annual KDNuggets poll of data scientists’ most-used tools in 2017.
  • In 2018, 66% of data scientists said they used Python on a daily basis, making it the most popular language among analytics professionals.
  • In 2021, it became the most widely used programming language, overtaking Java on the TIOBE index.

Furthermore, data science experts predict that this trend will continue.

Python for Data Science: How to Get Started:

Step 1: Learn the fundamentals of Python.

Everyone has to start somewhere. The first step is to learn the fundamentals of Python programming. (If you’re not already familiar with data science, you should start there.)This can be accomplished through an online course (such as Dataquest’s), data science bootcamps, self-directed learning, or university programmes. There is no right or wrong way to learn Python fundamentals. The key is to pick a path and stick to it.

Find an online community:

  • Join an online community to get help staying motivated. Most communities allow you to learn through questions posed to the group by you or others.
  • You can also network with other members of the community and develop relationships with industry professionals. This also increases your employment opportunities, as employee referrals account for 30% of all hires.
  • Opening a Kaggle account and joining a nearby Meetup group are two activities that many students find useful.
  • If you’re a Dataquest subscriber, you’ll have access to the Dataquest learner community, where you can get help from both current students and alums.

Step 2: Experiment with hands-on learning.

Hands-on learning is one of the most effective ways to accelerate your education.Experiment with Python projects.When you build small Python projects, you might be surprised at how quickly you pick up. Fortunately, almost every Dataquest course includes a project to help you learn more effectively. Here are a few examples:

  • Prison Break — Play around with Python and Jupyter Notebook to analyze a dataset of helicopter prison escapes.
  • Profitable App Store and Google Play Market App Profiles — In this guided project, you will work as a data analyst for a mobile app development company. Python will be used to provide value through practical data analysis.
  • Exploring Hacker News Posts — Work with a dataset of Hacker News submissions, a popular technology site.
  • Exploring eBay Car Sales Data — Use Python to work with a scraped dataset of used cars from eBay Kleinanzeigen, the German eBay website’s classifieds section.

Step 3: Become acquainted with Python data science libraries.

Some of the most useful Python libraries are NumPy, Pandas, Matplotlib, and Scikit-learn.

  • NumPy — A library that simplifies a wide range of mathematical and statistical operations; it also serves as the foundation for many features of the pandas library.
  • pandas — A Python library designed to make working with data easier. This is the backbone of much Python data science work.
  • Matplotlib — A visualization library that makes it simple to create charts from data.
  • Scikit-learn — The most popular Python library for machine learning work.
  • NumPy and Pandas are excellent for data exploration and play. Matplotlib is a data visualization library that generates graphs similar to those found in Microsoft Excel or Google Sheets.

Step 4: As you learn Python, create a data science portfolio.

A portfolio is essential for aspiring data scientists because it is one of the most important things hiring managers look for in a qualified candidate.These projects should involve working with a variety of datasets and sharing interesting insights that you discovered. Consider the following project types:

  • Data Cleaning Project — Because most real-world data requires cleaning, any project that involves dirty or “unstructured” data that you clean up and analyse will impress potential employers.
  • Data Visualization Project — Creating appealing, easy-to-read visualizations is a programming and design challenge, but if you succeed, your analysis will be significantly more useful. Having visually appealing charts in a project will help your portfolio stand out.
  • Machine Learning Project — If you want to work as a data scientist, you’ll need a project that demonstrates your ML abilities. You might want to create a few different machine learning projects, each focusing on a different algorithm.

Step 5: Make use of advanced data science techniques.

Finally, work on your skills. If you want to be sure you’ve covered all your data science bases before embarking on your data science adventure, you may attend advanced Python classes.Learn to use regression, classification, and k-means clustering models. You can also get started with machine learning by investigating bootstrapping models and building neural networks with Scikit-learn.

Is Python required in the field of data science?

Working as a data scientist with Python or R is an option. Each language has advantages and disadvantages. Both are common in the industry. Overall, Python is more popular, but R is dominant in some industries (particularly in academia and research).You will undoubtedly need to learn at least one of these two languages for data science.

Is Python preferable to R for data science?

This is a frequently debated topic in data science, but the true answer is that it depends on what you’re looking for and what you enjoy.R was designed for statistics and mathematics, but there are some incredible packages that make it incredibly simple to use for data science. It also has a very supportive online community.Python is a more versatile programming language. Many other disciplines can benefit from your Python skills. It’s also a tad more popular. Some would argue that it is easier to learn, but many R users would disagree.

Python Data Science Components :

Python Data Science Components

Considering Python to be the best programming language for data science, let us look at the specific components that make it so. Here is a list of concrete Python Data Science elements. Please take a look at these:

Exploration and analysis of data: Python has a plethora of built-in libraries that make it the best in this field. These libraries and features assist you in thoroughly exploring and analyzing the entire data structure. Python libraries such as Pandas, NumPy, and SciPy enable you to complete these tasks.

Data visualization: It is a language that allows you to name your data in a way that is self-explanatory. You can simply transform data into something more colorful. Python libraries such as Matplotlib, Seaborn, and Datashader can assist you with this task.

Data storage and big data frameworks: Big Data, as the name implies, is data that is either too large to reside on a single system or cannot be processed in the absence of a distributed environment. Python, in conjunction with Apache technologies, plays a significant role in completing this task. Some utilities and libraries that will assist you throughout the process include Apache Spark, Apache Hadoop, HDFS, Dask, and h5py/pytables.

Machine Learning: This can be classified as a supervised or unsupervised learning task. Scikit-learn is a library for performing classification, regression, clustering, and dimensionality reduction. In addition, Python has StatsModels, which is less actively developed but has some very useful features.

Deep learning: It is essentially a subset of machine learning that is commonly used with Keras. TensorFlow, in addition to Keras, is widely used for this purpose.

Others: Aside from the aforementioned processes or tasks, Python also performs other tasks such as natural language processing and image manipulation. Libraries such as nltk, Spacy, OpenCV/cv2, scikit-image, and Cython are heavily used for this.Python’s advantages and disadvantages in data sciencePython, like any other programming language or digital platform, has advantages and disadvantages. Before proceeding with this language for your Data Science career, consider all of its advantages and disadvantages.

Pros:

  • Python is adaptable, which means it is simple to use and quick to develop.
  • It is open source and has a thriving community.
  • It is extremely scalable.
  • Python has access to every library imaginable.
  • It is ideal for prototyping. This programming language allows you to accomplish more with less coding.

Cons:

  • Because it is an interpreted language, it may be slower than other programming languages.
  • Threading is not very good in Python due to the availability of GIL (Global Interpreter Lock).
  • Python does not have a native mobile environment. Some programmers regard it as a poor choice for mobile computing.
  • It has design constraints.
  • Python’s simplicity is also viewed as a weakness by some programmers. According to them, while simplicity can provide an easy start and a flat learning curve, it can also limit your ability to learn other complicated platforms.

The Benefits of the “Python for Data Science” Course:

There are numerous benefits to using Python for the Data Science course. It is versatile, which means it is simple to use and develop. Furthermore, only a small portion of this programming language is required to enter the data science industry. Mastering Python will also allow you to work on other web development projects. Aside from that, the availability of its numerous libraries is the panacea of its Data Science success. Its predefined libraries make life much easier than in other languages.People who have chosen Python for Data Science are undeniably advancing in their careers in a short period of time. There are numerous websites, such as Payscale, that clearly detail these people’s salary structures. In a nutshell, if you’re getting ready to row your boat into the ocean of Data Science, ditch the oars of other languages and choose Python to make it a Four Winns.

The ecosystem is entirely responsible for Python’s growth. Many volunteers are currently working on Python libraries, as the programming language has expanded its reach into the data science sector. This facilitates the development of advanced tools and processes. Python is easy, simple, powerful, and innovative due to its widespread use in a variety of contexts, some of which are unrelated to data science. R, without a doubt, is an optimized environment for data analysis, but it is a bit difficult to learn.

Conclusion :

Python began its journey in 1991 and has experienced exponential growth since then. Because of its built-in libraries and features, the programming language has proven to be one of the easiest to learn. Python is growing in popularity and is being used for a wide range of development and management tasks.Python is without a doubt the best programming language to get started as a Data Learner or Data Scientist. Its numerous libraries and simple structure make it more trustworthy for all newcomers to this field. Furthermore, learners can use this language for a variety of other web development purposes.When compared to the other programming languages used for data science, we conclude that Python is slightly superior to the majority of them. As a result, we strongly advise you to use Python 3 as your guide to future opportunities.

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